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Effect of heat pipe characteristics in the energy recovery ventilation system for positive pressure room
(2026-05-15)
Duangwana, Supaluek
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Chaiya, Wathanyu
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Laoariyawong, Anothai
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Chaimongkol, Jadsada
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Lapaphinya, Surasak
A system's high energy consumption is a drawback when it is used to create a positive pressure environment to reduce PM2.5. Air to air energy recovery ventilation systems (ERVs) using heat pipes are a feasible technology. This study aims to investigate the effect of installation patterns of heat pipes in air-to-air heat exchangers and the operating conditions of the system for generating a positive pressure environment. Flow characteristics of inline and staggered heat pipes with fins and without fins were investigated by using computational fluid dynamics. The different operating conditions varying inlet air temperature, air flow rate and heat pipe surface temperature were also studied. The results showed that the staggered heat pipes with fins introduced promising results. Outlet temperature was dominated by inlet air temperature and heat pipe surface temperature, while the pressure drop crossed ERV were relied on air flow rate. The promising results from the simulation were a guideline for constructing a practical heat exchanger that was integrated with positive pressure-generating equipment.
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Investigation of physiological disorder classification in mangosteen fruit using visible and shortwave near-infrared spectroscopy combined with machine learning
(2025-12-01)
Ruttanadech, Nuttapong
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Momin, Abdul
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Phetpan, Kittisak
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Chaichanyut, Montree
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Thongphut, Chitwadee
Accurate classification of physiological disorders in mangosteen fruit is crucial for ensuring production quality, safety, sustainability, and economic viability. This study investigates the application of visible and shortwave near-infrared (Vis/SWNIR) reflectance spectroscopy, combined with machine learning algorithms, to classify three primary disorders: normal fruit (NF), translucent flesh disorder (TFD), and TFD with yellow gummy latex (TFD & YGL). The study specifically examines the effects of light intensity, spectral pretreatments, and machine learning models on classification performance. Spectral data were collected using two light intensities (50 % and 100 % of a 150 W light source) and processed with three pretreatments: standard normal variate (SNV), second derivative Savitzky-Golay (SGD2), and a combination of SNV and SGD2. Random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms were used for classification. The SGD2 method improved differentiation, especially for the TFD & YGL class, in the 700–725 nm wavelength range, which is associated with xanthone content in the fruit's pericarp. Higher light intensity (100 %) significantly improved classification accuracy, achieving an overall accuracy of 0.71 and an average F1 score of 0.61 with the RF model. Despite these improvements, the model struggled to distinguish the TFD class from NF due to their similar spectral profiles. Overall, the Vis/SWNIR spectroscopy and machine learning combination shows strong potential for the non-destructive classification of mangosteen fruit disorders. Both light intensity and spectral pretreatments play critical roles in enhancing performance. Future studies should focus on improving spectral sensitivity to better capture internal fruit characteristics.
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Coconut Residue-Derived Nanoporous Carbon via Hydrothermal Carbonization for Nanoporous Carbon-Based Supercapacitor Electrodes
(2025-07-01)
Ruenroengrit, Kemchat
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Kunyuan, Jumpon
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Ruttanadech, Nuttapong
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Kaewtrakulchai, Napat
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Puengjinda, Pramote
The increasing demand for sustainable and cost-effective energy storage solutions has driven interest in biomass-derived carbon materials for supercapacitor electrodes. This study explores the valorization of coconut residue (CR), an abundant agricultural waste, as a carbon precursor for nanoporous carbon (NPC) production. NPC was synthesized via hydrothermal carbonization (HTC) of CR, followed by chemical activation using potassium hydroxide (KOH) at varying temperatures (700, 800, and 900 °C). The effects of activation temperature on the structure and electrochemical performance of the NPC were systematically investigated. The activated materials exhibited amorphous, highly porous structures, with surface areas increasing alongside activation temperature—reaching a maximum of 1969 m2 g−1 at 900 °C. Electrochemical characterization was conducted using a three-electrode setup through cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) in a 1 M Na<inf>2</inf>SO<inf>4</inf> electrolyte. The sample activated at 900 °C with a CR:KOH weight ratio of 1:2.5 achieved the highest specific capacitance of 52 F g−1 at a specific current of 1 A g−1. These findings underscore the potential of CR as a low-cost and sustainable raw material for fabricating efficient electrode materials in energy storage applications.
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NiO-YSZ anode composite material derived from mechano-chemical for solid oxide fuel cells application
(2025-06-01)
Srisuwan, Thanakorn
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Puengjinda, Pramote
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Kaewtrakulchai, Napat
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Chanpee, Sirayu
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Jadsadajerm, Supachai
This study investigates the effects of calcination time, milling duration, and sintering temperature on the properties of Nickel oxide and Yttria-stabilized zirconia (NiO-YSZ) anode composite materials for solid oxide fuel cell (SOFC) applications. NiO nanoparticles were synthesized from nickel (II) sulfate (Ni(II)SO<inf>4</inf>), with X-ray diffraction (XRD) confirming a face-centered cubic (FCC) structure. The crystallite size (21 nm) was achieved after 8 hours of calcination, while prolonged durations caused particle agglomeration. Ball-milling for 12 hours produced comparatively fine particles with an average of 706 nm, though extended milling led to grain growth and aggregation. Scanning electron microscopy (SEM) revealed nano aggregation. Sintering at 1200 °C improved the densification of the NiO-YSZ layer while increasing its porosity, which enhanced the reduction of NiO to metallic nickel (Ni). Electrochemical Impedance Spectroscopy (EIS) demonstrated lower impedance and improved electrochemical performance for cells cold-sintered at 1200 °C. These findings show the importance of optimizing processing conditions to enhance the performance of NiO-YSZ anode for SOFCs, offering valuable insights for advanced energy conversion technologies.
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Enhanced biodiesel purification using coffee husk bioadsorbents: The role of pyrolysis temperature, KOH activation, and adsorption efficiency
(2025-05-01)
Chungcharoen, Thatchapol
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Limmun, Warunee
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Srisang, Siriwan
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Phetpan, Kittisak
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Ruttanadech, Nuttapong
This study evaluates the performance of bioadsorbents derived from coffee husk pyrolyzed at temperatures of 600, 700, and 800 °C (CH600, CH700, and CH800), along with activated CH700 (ACH700), in biodiesel purification. The results indicate that CH700 significantly enhances biodiesel purity, with optimal purification conditions achieved at a dosage of 2 wt% CH700, a stirring rate of 400 rpm, and a contact time of 45 min. CH700 demonstrated modest performance, achieving approximately 20 % removal of methanol and water. However, after activation with potassium hydroxide (KOH), ACH700 demonstrated improved efficiency, achieving 96.92 % methanol removal and 39.46 % water removal. ACH700 also refined biodiesel quality to meet EN14214 standards and maintained a higher biodiesel yield compared to other adsorbents. The bioadsorption process is influenced by the chemical interactions between the surface functional groups of the bioadsorbent and the contaminants, which is further enhanced by the optimized pore structure of ACH700. The use of ACH700 represents a novel and highly effective approach to biodiesel purification, combining both technical efficiency and economic feasibility. Furthermore, the valorization of agricultural waste adds significant environmental benefits, reinforcing the potential of ACH700 for large-scale biodiesel production.
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Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning
(2023-03-01)
Ruttanadech, Nuttapong
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Phetpan, Kittisak
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Srisang, Naruebodee
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Srisang, Siriwan
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Chungcharoen, Thatchapol
Near-infrared (NIR) spectral-based classification of Aspergillus ochraceous contamination in the Robusta green coffee bean was investigated. Six different learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (Tree), Naive Bayes (NB), and quadratic discriminant analysis (QDA), were applied for the investigating purpose. Four classes of fungal contamination on coffee beans, non-fungal contaminated beans on day 1 and day 3 (NCB-D1 and NCB-D3) and fungal contaminated beans on day 1 and day 3 (CB-D1 and CB-D3), were set for the classification intention. Based on the 6 learning algorithms, the Tree approach was optimal, displaying a training accuracy of 97.5%. As proven by the testing dataset, the classification accuracy of the Tree was also at 97.5%. With this number, the Tree could correctly classify 100% between the contaminated and non-contaminated coffee beans. These findings exhibit the potential of the NIR spectroscopy accompanied by machine learning for the early detection of fungal contamination in green coffee beans.
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Transflection Near-infrared Spectroscopy Combined with Machine Learning for Mechanical Stability Time Evaluation in Concentrated Rubber Latex
(2023-01-01)
Suttho, Pisit
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Phetpan, Kittisak
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Sirisomboon, Panmanas
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Lim, Chin Hock
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Ruttanadech, Nuttapong
This study aims to apply near-infrared spectroscopy (NIRS) in transflection mode combined with a machine learning approach to evaluate the mechanical stability time (MST) in Para concentrated rubber latex. Four supervised learning algorithms, including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR), were employed to relate the NIR spectra with the MST degree of the latex samples. A comparison of predictive performance among these different algorithms was performed. The RFR model exhibited the best fitting performance with a coefficient of determination for calibration (R2) and root mean square error of calibration (RMSEC) of 0.95 and 37 seconds, respectively. In addition, the RFR-based model outperformed all others with its predictive performance, presenting coefficient of determination for prediction (r2) and root mean square error of prediction (RMSEP) of 0.64 and 91 seconds, respectively. Based on these results, this study could imply that the relationship between the NIR spectra and the change in the MST degree of the samples tends to be nonlinear.
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A study on binderless co-pelletization of industrial rice-powder wastes and teak sawdust at low and elevated temperatures
(2022-12-01)
Munsin, Ronnachart
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Udtasri, Jakkarin
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Topaiboul, Subongkoj
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Kowtakul, Pichet
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Yeunyongkul, Pracha
The objectives of this work were to investigate the effect of rice-powder wastes on binding characteristics of pellet at low temperature (25 °C) and to investigate effect of the elevated temperature of die (80 and 140 °C) on the quality of co-pelletized fuel. Mixing ratios of raw materials, i.e. teak sawdust and rice-powder wastes, were tested at 1:0.5, 1:1, 1:2 and 1:3. The results showed that the mixing ratio of 1:1 was optimal at low temperature for binding considered from a trade-off between the increase in friction versus the increase in density and strength. The qualities of pelletized fuel were improved by the elevated temperatures, especially at 140 °C, resulted in the increase in density, compressive strength and energy density, and the decrease in adhesive friction and moisture content. While the color change of pellet was highly depended on die temperature (10 at 80 °C and over 15 at 140 °C), the color change of pellet was not dominated by type of rice-powder wastes.
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Charcoal briquette production from waste in the coffee production process using hydrothermal and torrefaction techniques: A comparative study with carbonization technique
(2022-10-20)
Srisang, Siriwan
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Phetpan, Kittisak
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Ruttanadech, Nuttapong
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Limmun, Warunee
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Youryon, Pannipa
This research aimed to study charcoal briquette production from coffee production waste, i.e., coffee parchment (CP) and coffee cherry pulp (CCP). The carbonization technique (CT) was studied in five mixtures (CP and CCP ≈ 0–90%) and three pressures (1000–1600 psi) to determine the appropriate conditions. The torrefaction technique (TT) and hydrothermal technique (HT) were subsequently performed under proper conditions from the CT to investigate further the fit temperatures (200–260 °C) and reaction times (40–120 min). The fuel characteristics were examined regarding the calorific value (CV), proximate and ultimate analyses, mechanical properties, and utilization properties. The results demonstrated the notable influence of interaction between the mixture and pressure factors and individual mixture on the fuel properties, whereas personal pressure have an insignificant effect. The ratio of CP ≈ 90% and binder ≈10% at a pressure of 1600 psi in the CT prepared appropriate fuel properties (calorific value ≈ 27 MJ/kg, fixed carbon content ≈ 65%). Interestingly, almost all conditions of the TT and HT provided greenhouse gas emissions lower than the CT. The TT and HT at 260 °C for 120 min provided high calorific values (25–26 MJ/kg) with other fuel characteristics in the acceptable standard, except for the fixed carbon content.
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Prototype of portable robusta coffee harvesting machine
(2021-11-19)
Ruttanadech, Nuttapong
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Chungcharoen, Thatchapol
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Sansiribhan, Sansanee
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Munsin, Ronnachart
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Thuwapanichayanan, Ratiya
The limitations of machine size and cost are main problems for coffee harvesting using machine picking. Therefore, the prototype of Robusta coffee harvesting machine with portable and low cost was designed and created in this research. Moreover, the effects of bristle hardness, bristle size and brush rotation speed on the coffee harvesting efficiency were investigated. The experimental results showed that the prototype of portable Robusta coffee harvesting machine was effective to harvest the Robusta coffee. It provided the highest coffee harvesting efficiency of 75.24% by using the bristle hardness of 75 shore A, bristle size of 8 mm and brush rotation speed of 700 rpm. The bristle hardness, bristle size and brush rotation speed were significantly affected the coffee harvesting efficiency. The coffee harvesting efficiency was increased when increasing the bristle hardness from 60 shore A to 75 shore A and the brush rotation speed from 600 rpm to 700 rpm. On the other hand, the increase of bristle size from 8 mm to 10 mm provided the lower coffee harvesting efficiency.

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